About Us

We design, build, and study systems that support social interactions in online and physical spaces.

We utilize a variety of methods from mining data on social media to conducting controlled experiments to interviewing users. Our work aims to understand the significance of people’s digital traces and to leverage this information for positive social good.

Projects

Locally-Connected Experiences

As part of the AOL Connected Experiences Laboratory, we look at how data from mobile devices, sensors, as well as new cryptographic techniques and protocols can enable a socio-technical infrastructure to provide awareness, trust and meaningful connections between physically co-located individuals, including buildings, offices, and public spaces. Such infrastructure will empower people to make better connections and communication in their local communities, with long term impact on participation and democracy.

Attention to Online Media

The goal of this project is to advance our understanding of the psychological mechanisms behind people's attention, as reflected through their interactions with digital content. In particular, we focus on the context of actions that people take online without any experimental intervention and examine how context affects behavior. We draw on theories from a wide range of fields to address questions that pertain to individual's attention to content, expectations for attention from others and the value in getting that attention. To that end, we harness machine learning methods as well as language and statistical modeling to analyze signals of human attention as they occurs naturally outside of lab settings.

Publications

Xiao Ma, Trishala Neeraj, and Mor Naaman. A Computational Approach to Perceived Trustworthiness of Airbnb Host Profiles. In Proceedings, International AAAI Conference on Web and Social Media. (ICWSM 2017), May 2017, Montreal, Canada

Building on our previous work (see below by Ma et al. CSCW 2017), we developed a novel computational framework to predict the perceived trustworthiness of host profile texts in the context of online lodging marketplaces. We developed a dataset of 4,180 Airbnb host profiles annotated with perceived trustworthiness, and also provide insights into the linguistic factors that contribute to higher and lower perceived trustworthiness for profiles of different lengths.

Website measures of engagement captured from millions of users, such as in-page scrolling and viewport position, can provide deeper understanding of attention than possible with simpler measures, such as dwell time. Using data from 1.2M news reading sessions, we examine and evaluate three increasingly sophisticated models of sub-document attention computed from viewport time, the time a page component is visible on the user display. Our approach supports refined large-scale measurement of user engagement at a level previously available only from lab-based eye-tracking studies.

We describe the design and implementation of MoveMeant, a system aimed to increase local community awareness through shared location traces. We report findings from interviews with residents in the Bronx, New York City who participated in a deployment of MoveMeant over a 6-week period.

We interviewed users of two anonymous social applications, Secret and Mimi, both of which allowed people to share information anonymously with friends. Our findings show that although users feel more comfortable sharing information on these “tie-based” anonymous applications, they are still concerned about being identified, and at the same time, engage in sometimes elaborate attempts to uncover the identities of others.

We quantitatively examined the factors contributing to feedback expectations and find that fulfilling expectations is linked to connectedness, an important ingredient for well-being. By conducting two large surveys on Facebook.com we find that people report higher expectations on posts they evaluated as more important, and to a lesser extent more personal. Expectations varied across people and friendships, most notably by recency of communication, geographical proximity, and the type of relationship (e.g. family, co-worker). The study provides a conceptual framework for thinking about feedback expectations in social media settings and a computational model for utilizing expectations in the design of social systems.

We conducted the first large-scale mixed-method analysis of Airbnb host profiles, by categorizing the types of information hosts share in their profiles, and assessing how trustworthy their profiles are perceived to be. We found that hosts do not always follow the prompts by Airbnb website, but instead disclose information that reduces the uncertainty of anticipated future interaction. The language of hospitality, i.e., making direct promises to take care of guests, was found to be the most effective in establishing perceived trustworthiness.

Peer-to-peer indirect exchange services, such as Peerby and NeighborGoods, do not seem to have been as widely adopted as direct exchange systems, such as Uber and AirBnb. Building upon the results of interviews with 37 residents of New York City, a survey with 195 respondents, previous technology acceptance models, critical mass theory, and prior research on peer economies, we propose a technology acceptance model for indirect exchange systems that includes generalized trust and ease of coordination.

Xiao Ma, Emily Sun, Mor Naaman. What Happens in happn: The Warranting Power of Location History in Online Dating. In Proceedings, ACM International Conference on Computer-Supported Cooperative Work. (CSCW 2017), February 2017, Portland, USA

We interviewed users of a novel mobile dating application, happn, which shows users the number of times they crossed path with potential matches. We show that users assigned significant meaning to the minimal cues available from location history information. At the same time, users have concerns about security and recognition by known others as a result of sharing personal location history.

We conducted an experiment to study the relationship between content intimacy and self-disclosure in social media, and how anonymity and audience type (social ties vs. people nearby) moderate that relationship.

The study identified a unique pattern of engagement that accompanies posting on Facebook. Using observational data analysis we show that after posting content, people visit the site more often, are more attentive to content from friends, and even interact more with friends content.

Raz Schwarz, Mor Naaman, Rannie Teodoro. Editorial Algorithms: Using Social Media to Discover and Report Local News. In Proceedings, ACM International Conference on Web Logs and Social Media. (ICWSM 2015), May 2015, Cambridge, England

We discuss CityBeat, a large-screen visualization that builds on machine learning techniques to expose hyper-local events in New York City from social media data. We deployed it with some national and local media and describe the gap between the journalistic needs and what our algorithm could provide.

Using data from users of Last.fm and Twitter, we design and evaluate a novel measure for computing diversity of musical tastes, and investigate its relationships with socioeconomic status and personal traits such as openness and degree of interest in music.

David Flatow, Mor Naaman, Ke Eddie Xie, Yana Volkovich, Yaron Kanza. On the Accuracy of Hyper-local Geotagging of Social Media Content. In Proceedings, the ACM International Conference on Web Search and Data Mining (WSDM 2015). March 2015, Shanghai, China.

Can we use geotagged social media to identify phrases that correspond to hyper-local geographic features? Does the geographic spread of such phrases differ between services or device types? (the answer to both is yes).

Content analysis of tweets expressing loneliness exposes some key patterns in how people talk about loneliness on Twitter, as well as when such posts are likely to receive a response from friends and followers.

We developed methods to identify how people talk about real-world activities on social media. By examining activities such as nightlife, food or shopping we peek at the fundamental rhythm of human behavior at a city level and observe how it was disrupted during Hurricane Sandy.